﻿This dissertation proposes a statistical-structural character modeling method based
on the Markov Random Fields(MRFs) in the Handwritten Chinese Character Recognition(HCCR) problem. In the MRF framework, we view the character recognition as
a labeling problem, as how well a given observation matches a character model. The
MRF framework can represent both statistical and structural information of the Chinese
character by the neighborhood systems and clique potentials. The neighborhood system
denotes the most important stroke relationships. The clique potential is composed by
prior clique potential based on our prior knowledge and likelihood clique potential represents
both statistical and structural information, which is derived from Gaussian Mixture
Models(GMMs). We add the radical information into our prior knowledge, thus form a
hierarchical character structure in which radicals constitute characters, and strokes constitute
radicals. With the help of radical structure, we can easily grasp the most important
stroke relationships.
We implemented a real-world application of character recognition. In the proposed
HCCR system, we extract candidate strokes from character image by minimizing the
single-site likelihood clique potentials, and find the best structural match between candidate
strokes and stroke models by the relaxation labeling algorithm. The experiments
done on the Korea Advanced Institute of Science and Technology (KAIST) character
database demonstrate the practicability of proposed approaches.